Training course · Generative AI · Manufacturing

Generative AI for Manufacturing Teams

Vendor-neutral, hands-on day: how language models work, where they go wrong and how to roll out AI successfully in your team.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Generative AI for Manufacturing Teams” is a hands-on course for roles such as Production and plant managers, Shift leaders and supervisors and Manufacturing and process engineers. Rather than learning Generative AI in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Manufacturing.

Generative AI has reached most organisations, yet teams often lack a shared understanding of what language models can actually do and where their limits lie. In this vendor-neutral course you will learn how large language models generate text, why they hallucinate and how careful prompting, context and source checking lead to reliable results. Rather than relying on any provider's marketing claims, you will work with transparent criteria that help you compare tools and judge their fit for your own tasks.

The second focus is everyday implementation. You will identify concrete use cases in your area of work, weigh benefit, risk and effort, and draft a realistic roll-out plan for your team. This covers ground rules for handling confidential data, a practical prompt library and ways to build acceptance and make results measurable. Throughout the day you work with examples from your own field, so you leave with an actionable roadmap rather than a set of abstract ideas.

Manufacturers face cost pressure, skills shortages, complex product variants and high expectations for on-time delivery. Generative AI adds a new layer to existing Industry 4.0 solutions: AI assistants make knowledge from manuals, fault logs and work instructions accessible, support shift handovers, summarise KPIs from MES and ERP exports and help with root cause analysis using methods such as 5 Whys or Ishikawa. AI agents and automation create shift reports, analyse machine breakdowns and prepare maintenance orders. Experienced staff in particular benefit when their knowledge is documented and made available to new colleagues. Adoption succeeds when it starts with concrete shop-floor problems, involves works councils and health and safety early and keeps production data secure.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Production and plant managers
  • Shift leaders and supervisors
  • Manufacturing and process engineers
  • Maintenance teams
  • Lean and operational excellence leads

Typical challenges we address

  • Capturing and passing on the know-how of experienced staff
  • Analysing breakdowns quickly and reducing downtime
  • Presenting KPIs such as OEE from various systems in an understandable way
  • Keeping work instructions current and clear across many variants

What you will be able to do

  • You can explain clearly how language models generate text and why hallucinations occur.
  • You can write prompts that consistently deliver useful, verifiable results.
  • You can compare AI tools against transparent criteria covering data protection, features and cost.
  • You can identify and prioritise concrete use cases for your area of work.
  • Use AI for knowledge management, root cause analysis and shift reporting in production.
  • Analyse production KPIs with AI and derive improvement actions.
  • Take data security, co-determination and occupational safety into account in manufacturing AI projects.

Course outline

Module 1 How large language models work
  • Tokens, context windows and probabilities: what happens when a model generates text
  • Training knowledge versus web search versus your own documents (RAG)
  • Text, images, audio and code: what multimodal models can do today
  • Realistically assessing typical strengths and weaknesses in office work
Module 2 Spotting hallucinations and getting reliable output
  • Why models produce plausible-sounding errors and when the risk increases
  • Prompting techniques: role, context, examples, output format and step-by-step instructions
  • Requesting sources, cross-checking facts and introducing a four-eyes principle
  • Scoping tasks so that AI assists rather than decides
Module 3 Choosing tools without vendor bias
  • Overview of chat assistants, AI built into office suites and specialist tools
  • Criteria: data processing, hosting location, admin features, integrations and pricing model
  • Enterprise licences, API access and locally hosted open-source models compared
  • Building a simple scoring matrix for your own tool selection
Module 4 Identifying use cases in your field
  • Systematically capturing recurring writing, research and analysis tasks
  • Rating benefit, risk and implementation effort for each use case
  • Distinguishing quick wins from strategic initiatives
  • Creating a prioritised use-case backlog for your team
Module 5 Rolling out generative AI in your team
  • Usage rules: which data may go into which tool and who signs off results
  • Building and maintaining a shared prompt library and templates
  • AI champions, training formats and peer exchanges to embed new habits
  • Measuring success: documenting time saved, quality and user satisfaction
Module 6 Practice lab: Manufacturing
  • Shop-floor knowledge assistant
  • Root cause analysis
  • Shift reports and handovers
  • Work instructions
  • KPI analysis
  • Maintenance preparation
Module 7 Data protection, quality & transfer to daily work
  • Production data and engineering know-how are trade secrets and belong only in approved AI environments; in Germany, AI-based evaluation of employee performance or behaviour requires works council co-determination. AI components in safety-related machine functions fall under the EU Machinery Regulation and may be high-risk under the EU AI Act.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Shop-floor knowledge assistant

An AI assistant answers questions about machines, changeovers and fault patterns based on manuals, breakdown history and work instructions, always citing the source for verification.

Root cause analysis

AI structures breakdowns and quality issues using 5 Whys, Ishikawa or 8D, suggests possible causes from similar cases and helps document actions and responsibilities.

Shift reports and handovers

Notes, machine data and fault reports are turned into structured shift reports with output, breakdowns and open points, making handover to the next shift easier.

Work instructions

AI turns process descriptions, photos and expert interviews into illustrated, multilingual work instructions and checklists in plain language and keeps variants consistent.

KPI analysis

MES and ERP exports are analysed, OEE losses broken down by availability, performance and quality and anomalies explained clearly for shop-floor meetings.

Maintenance preparation

Automations collect fault reports; AI groups recurring failures, drafts maintenance orders and prioritises actions based on frequency and impact, before maintenance planning takes them over.

Prerequisites

No technical background is required. It helps to bring along typical tasks from your day-to-day work that you would like to support with AI. You work in production, production planning or maintenance and know the processes at your plant.

Technical requirements

You will need a laptop with an up-to-date browser and internet access. A free account with a common AI assistant is sufficient; company licences are welcome.

Data protection & compliance

Production data and engineering know-how are trade secrets and belong only in approved AI environments; in Germany, AI-based evaluation of employee performance or behaviour requires works council co-determination. AI components in safety-related machine functions fall under the EU Machinery Regulation and may be high-risk under the EU AI Act.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Fri, 27 November 2026 Online Request
  • Thu, 10 December 2026 Berlin Request
  • Mon, 14 December 2026 Online Request
  • Tue, 12 January 2027 Online Request
  • Mon, 18 January 2027 Stuttgart Request
  • Fri, 29 January 2027 Online Request

Frequently asked questions

Is the course tied to a specific AI tool?

No. The training is deliberately vendor-neutral. You work with widely used chat assistants and learn principles that apply to all major language models. That way you can transfer your skills to whichever tool your organisation has approved and evaluate new offerings yourself using clear criteria instead of relying on marketing statements.

How is confidential company data handled during the course?

For the exercises you use anonymised or fictitious examples from your area of work. At the same time you learn which categories of data may be entered into which tools, what to look for regarding data processing agreements and use of inputs for training, and how to set out these rules clearly for your team.

Will we leave with something concrete to implement?

Yes. You leave with a prioritised use-case backlog, a scoring matrix for tool selection, tested prompt templates and a draft set of team usage rules. These materials are designed so you can take them straight into internal discussions with management, IT and your data protection officer.

Do we need to connect AI to our machines?

Not necessarily. Many use cases, such as knowledge assistants, work instructions or root cause analysis, work with documents and MES or ERP exports. Connecting directly to machine data is a later step that requires IT and OT security. In the course we start with use cases you can implement quickly.

How do we gain acceptance from the workforce?

By making AI visibly ease work, for example through faster troubleshooting or simpler documentation, rather than being perceived as a monitoring tool. Involve employees and their representatives early, start with pilot areas and clearly define which data will not be used for performance evaluation.

How much does “Generative AI for Manufacturing Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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